System and method for improving space efficiency by compressing multi-block aggregates

ABSTRACT

A method, computer program product, and computer system for identifying a plurality of blocks. At least one heuristic associated with at least a portion of the plurality of blocks may be determined. It may be determined whether to compress at least the portion of the plurality of blocks based upon, at least in part, the at least one heuristic. At least the portion of the plurality of blocks may be compressed based upon, at least in part, the at least one heuristic.

BACKGROUND

Some storage systems may operate on, e.g., 8K block granularity. Thismay generally mean that compression is also done on 8K blocks.Typically, such compression is performed on individual blocks.

BRIEF SUMMARY OF DISCLOSURE

In one example implementation, a method, performed by one or morecomputing devices, may include but is not limited to identifying aplurality of blocks. At least one heuristic associated with at least aportion of the plurality of blocks may be determined. It may bedetermined whether to compress at least the portion of the plurality ofblocks based upon, at least in part, the at least one heuristic. Atleast the portion of the plurality of blocks may be compressed basedupon, at least in part, the at least one heuristic.

One or more of the following example features may be included. The atleast one heuristic may include a distance metric associated with atleast the portion of the plurality of blocks. The at least one heuristicmay include a digital entropy value associated with at least the portionof the plurality of blocks. The digital entropy value may determinewhether to compress at least the portion of the plurality of blocks. Atleast the portion of the plurality of blocks with a similar digitalentropy value may be compressed together. A first level of compressionmay be used to compress at least the portion of the plurality of blocksduring background operations. A second level of compression may be usedto compress at least the portion of the plurality of blocks duringnon-background operations.

In another example implementation, a computing system may include one ormore processors and one or more memories configured to performoperations that may include but are not limited to identifying aplurality of blocks. At least one heuristic associated with at least aportion of the plurality of blocks may be determined. It may bedetermined whether to compress at least the portion of the plurality ofblocks based upon, at least in part, the at least one heuristic. Atleast the portion of the plurality of blocks may be compressed basedupon, at least in part, the at least one heuristic.

One or more of the following example features may be included. The atleast one heuristic may include a distance metric associated with atleast the portion of the plurality of blocks. The at least one heuristicmay include a digital entropy value associated with at least the portionof the plurality of blocks. The digital entropy value may determinewhether to compress at least the portion of the plurality of blocks. Atleast the portion of the plurality of blocks with a similar digitalentropy value may be compressed together. A first level of compressionmay be used to compress at least the portion of the plurality of blocksduring background operations. A second level of compression may be usedto compress at least the portion of the plurality of blocks duringnon-background operations.

In another example implementation, a computer program product may resideon a computer readable storage medium having a plurality of instructionsstored thereon which, when executed across one or more processors, maycause at least a portion of the one or more processors to performoperations that may include but are not limited to identifying aplurality of blocks. At least one heuristic associated with at least aportion of the plurality of blocks may be determined. It may bedetermined whether to compress at least the portion of the plurality ofblocks based upon, at least in part, the at least one heuristic. Atleast the portion of the plurality of blocks may be compressed basedupon, at least in part, the at least one heuristic.

One or more of the following example features may be included. The atleast one heuristic may include a distance metric associated with atleast the portion of the plurality of blocks. The at least one heuristicmay include a digital entropy value associated with at least the portionof the plurality of blocks. The digital entropy value may determinewhether to compress at least the portion of the plurality of blocks. Atleast the portion of the plurality of blocks with a similar digitalentropy value may be compressed together. A first level of compressionmay be used to compress at least the portion of the plurality of blocksduring background operations. A second level of compression may be usedto compress at least the portion of the plurality of blocks duringnon-background operations.

The details of one or more example implementations are set forth in theaccompanying drawings and the description below. Other possible examplefeatures and/or possible example advantages will become apparent fromthe description, the drawings, and the claims. Some implementations maynot have those possible example features and/or possible exampleadvantages, and such possible example features and/or possible exampleadvantages may not necessarily be required of some implementations.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is an example diagrammatic view of a compression process coupledto an example distributed computing network according to one or moreexample implementations of the disclosure;

FIG. 2 is an example diagrammatic view of a storage system of FIG. 1according to one or more example implementations of the disclosure;

FIG. 3 is an example diagrammatic view of a storage target of FIG. 1according to one or more example implementations of the disclosure;

FIG. 4 is an example flowchart of a compression process according to oneor more example implementations of the disclosure; and

FIG. 5 is an example diagrammatic view of a basic mapping structureaccording to one or more example implementations of the disclosure.

Like reference symbols in the various drawings indicate like elements.

DETAILED DESCRIPTION System Overview:

In some implementations, the present disclosure may be embodied as amethod, system, or computer program product. Accordingly, in someimplementations, the present disclosure may take the form of an entirelyhardware implementation, an entirely software implementation (includingfirmware, resident software, micro-code, etc.) or an implementationcombining software and hardware aspects that may all generally bereferred to herein as a “circuit,” “module” or “system.” Furthermore, insome implementations, the present disclosure may take the form of acomputer program product on a computer-usable storage medium havingcomputer-usable program code embodied in the medium.

In some implementations, any suitable computer usable or computerreadable medium (or media) may be utilized. The computer readable mediummay be a computer readable signal medium or a computer readable storagemedium. The computer-usable, or computer-readable, storage medium(including a storage device associated with a computing device or clientelectronic device) may be, for example, but is not limited to, anelectronic, magnetic, optical, electromagnetic, infrared, orsemiconductor system, apparatus, device, or any suitable combination ofthe foregoing. More specific examples (a non-exhaustive list) of thecomputer-readable medium may include the following: an electricalconnection having one or more wires, a portable computer diskette, ahard disk, a random access memory (RAM), a read-only memory (ROM), anerasable programmable read-only memory (EPROM or Flash memory), anoptical fiber, a portable compact disc read-only memory (CD-ROM), anoptical storage device, a digital versatile disk (DVD), a static randomaccess memory (SRAM), a memory stick, a floppy disk, a mechanicallyencoded device such as punch-cards or raised structures in a groovehaving instructions recorded thereon, a media such as those supportingthe internet or an intranet, or a magnetic storage device. Note that thecomputer-usable or computer-readable medium could even be a suitablemedium upon which the program is stored, scanned, compiled, interpreted,or otherwise processed in a suitable manner, if necessary, and thenstored in a computer memory. In the context of the present disclosure, acomputer-usable or computer-readable, storage medium may be any tangiblemedium that can contain or store a program for use by or in connectionwith the instruction execution system, apparatus, or device.

In some implementations, a computer readable signal medium may include apropagated data signal with computer readable program code embodiedtherein, for example, in baseband or as part of a carrier wave. In someimplementations, such a propagated signal may take any of a variety offorms, including, but not limited to, electro-magnetic, optical, or anysuitable combination thereof. In some implementations, the computerreadable program code may be transmitted using any appropriate medium,including but not limited to the internet, wireline, optical fibercable, RF, etc. In some implementations, a computer readable signalmedium may be any computer readable medium that is not a computerreadable storage medium and that can communicate, propagate, ortransport a program for use by or in connection with an instructionexecution system, apparatus, or device.

In some implementations, computer program code for carrying outoperations of the present disclosure may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Java®, Smalltalk, C++ or the like.Java® and all Java-based trademarks and logos are trademarks orregistered trademarks of Oracle and/or its affiliates. However, thecomputer program code for carrying out operations of the presentdisclosure may also be written in conventional procedural programminglanguages, such as the “C” programming language, PASCAL, or similarprogramming languages, as well as in scripting languages such asJavascript, PERL, or Python. The program code may execute entirely onthe user's computer, partly on the user's computer, as a stand-alonesoftware package, partly on the user's computer and partly on a remotecomputer or entirely on the remote computer or server. In the latterscenario, the remote computer may be connected to the user's computerthrough a local area network (LAN) or a wide area network (WAN), or theconnection may be made to an external computer (for example, through theinternet using an Internet Service Provider). In some implementations,electronic circuitry including, for example, programmable logiccircuitry, field-programmable gate arrays (FPGAs) or other hardwareaccelerators, micro-controller units (MCUs), or programmable logicarrays (PLAs) may execute the computer readable programinstructions/code by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present disclosure.

In some implementations, the flowchart and block diagrams in the figuresillustrate the architecture, functionality, and operation of possibleimplementations of apparatus (systems), methods and computer programproducts according to various implementations of the present disclosure.Each block in the flowchart and/or block diagrams, and combinations ofblocks in the flowchart and/or block diagrams, may represent a module,segment, or portion of code, which comprises one or more executablecomputer program instructions for implementing the specified logicalfunction(s)/act(s). These computer program instructions may be providedto a processor of a general purpose computer, special purpose computer,or other programmable data processing apparatus to produce a machine,such that the computer program instructions, which may execute via theprocessor of the computer or other programmable data processingapparatus, create the ability to implement one or more of thefunctions/acts specified in the flowchart and/or block diagram block orblocks or combinations thereof. It should be noted that, in someimplementations, the functions noted in the block(s) may occur out ofthe order noted in the figures (or combined or omitted). For example,two blocks shown in succession may, in fact, be executed substantiallyconcurrently, or the blocks may sometimes be executed in the reverseorder, depending upon the functionality involved.

In some implementations, these computer program instructions may also bestored in a computer-readable memory that can direct a computer or otherprogrammable data processing apparatus to function in a particularmanner, such that the instructions stored in the computer-readablememory produce an article of manufacture including instruction meanswhich implement the function/act specified in the flowchart and/or blockdiagram block or blocks or combinations thereof.

In some implementations, the computer program instructions may also beloaded onto a computer or other programmable data processing apparatusto cause a series of operational steps to be performed (not necessarilyin a particular order) on the computer or other programmable apparatusto produce a computer implemented process such that the instructionswhich execute on the computer or other programmable apparatus providesteps for implementing the functions/acts (not necessarily in aparticular order) specified in the flowchart and/or block diagram blockor blocks or combinations thereof.

Referring now to the example implementation of FIG. 1, there is showncompression process 10 that may reside on and may be executed by acomputer (e.g., computer 12), which may be connected to a network (e.g.,network 14) (e.g., the internet or a local area network). Examples ofcomputer 12 (and/or one or more of the client electronic devices notedbelow) may include, but are not limited to, a storage system (e.g., aNetwork Attached Storage (NAS) system, a Storage Area Network (SAN)), apersonal computer(s), a laptop computer(s), mobile computing device(s),a server computer, a series of server computers, a mainframecomputer(s), or a computing cloud(s). As is known in the art, a SAN mayinclude one or more of the client electronic devices, including a RAIDdevice and a NAS system. In some implementations, each of theaforementioned may be generally described as a computing device. Incertain implementations, a computing device may be a physical or virtualdevice. In many implementations, a computing device may be any devicecapable of performing operations, such as a dedicated processor, aportion of a processor, a virtual processor, a portion of a virtualprocessor, portion of a virtual device, or a virtual device. In someimplementations, a processor may be a physical processor or a virtualprocessor. In some implementations, a virtual processor may correspondto one or more parts of one or more physical processors. In someimplementations, the instructions/logic may be distributed and executedacross one or more processors, virtual or physical, to execute theinstructions/logic. Computer 12 may execute an operating system, forexample, but not limited to, Microsoft® Windows®; Mac® OS X®; Red Hat®Linux®, Windows® Mobile, Chrome OS, Blackberry OS, Fire OS, or a customoperating system. (Microsoft and Windows are registered trademarks ofMicrosoft Corporation in the United States, other countries or both; Macand OS X are registered trademarks of Apple Inc. in the United States,other countries or both; Red Hat is a registered trademark of Red HatCorporation in the United States, other countries or both; and Linux isa registered trademark of Linus Torvalds in the United States, othercountries or both).

In some implementations, as will be discussed below in greater detail, acompression process, such as compression process 10 of FIG. 1, mayidentify a plurality of blocks. At least one heuristic associated withat least a portion of the plurality of blocks may be determined. It maybe determined whether to compress at least the portion of the pluralityof blocks based upon, at least in part, the at least one heuristic. Atleast the portion of the plurality of blocks may be compressed basedupon, at least in part, the at least one heuristic.

In some implementations, the instruction sets and subroutines ofcompression process 10, which may be stored on storage device, such asstorage device 16, coupled to computer 12, may be executed by one ormore processors and one or more memory architectures included withincomputer 12. In some implementations, storage device 16 may include butis not limited to: a hard disk drive; all forms of flash memory storagedevices; a tape drive; an optical drive; a RAID array (or other array);a random access memory (RAM); a read-only memory (ROM); or combinationthereof. In some implementations, storage device 16 may be organized asan extent, an extent pool, a RAID extent (e.g., an example 4D+1P R5,where the RAID extent may include, e.g., five storage device extentsthat may be allocated from, e.g., five different storage devices), amapped RAID (e.g., a collection of RAID extents), or combinationthereof.

In some implementations, network 14 may be connected to one or moresecondary networks (e.g., network 18), examples of which may include butare not limited to: a local area network; a wide area network or othertelecommunications network facility; or an intranet, for example. Thephrase “telecommunications network facility,” as used herein, may referto a facility configured to transmit, and/or receive transmissionsto/from one or more mobile client electronic devices (e.g., cellphones,etc.) as well as many others.

In some implementations, computer 12 may include a data store, such as adatabase (e.g., relational database, object-oriented database,triplestore database, etc.) and may be located within any suitablememory location, such as storage device 16 coupled to computer 12. Insome implementations, data, metadata, information, etc. describedthroughout the present disclosure may be stored in the data store. Insome implementations, computer 12 may utilize any known databasemanagement system such as, but not limited to, DB2, in order to providemulti-user access to one or more databases, such as the above notedrelational database. In some implementations, the data store may also bea custom database, such as, for example, a flat file database or an XMLdatabase. In some implementations, any other form(s) of a data storagestructure and/or organization may also be used. In some implementations,compression process 10 may be a component of the data store, astandalone application that interfaces with the above noted data storeand/or an applet/application that is accessed via client applications22, 24, 26, 28. In some implementations, the above noted data store maybe, in whole or in part, distributed in a cloud computing topology. Inthis way, computer 12 and storage device 16 may refer to multipledevices, which may also be distributed throughout the network.

In some implementations, computer 12 may execute a storage managementapplication (e.g., storage management application 21), examples of whichmay include, but are not limited to, e.g., a storage system application,a cloud computing application, a data synchronization application, adata migration application, a garbage collection application, or otherapplication that allows for the implementation and/or management of datain a clustered (or non-clustered) environment (or the like). In someimplementations, compression process 10 and/or storage managementapplication 21 may be accessed via one or more of client applications22, 24, 26, 28. In some implementations, compression process 10 may be astandalone application, or may be an applet/application/script/extensionthat may interact with and/or be executed within storage managementapplication 21, a component of storage management application 21, and/orone or more of client applications 22, 24, 26, 28. In someimplementations, storage management application 21 may be a standaloneapplication, or may be an applet/application/script/extension that mayinteract with and/or be executed within compression process 10, acomponent of compression process 10, and/or one or more of clientapplications 22, 24, 26, 28. In some implementations, one or more ofclient applications 22, 24, 26, 28 may be a standalone application, ormay be an applet/application/script/extension that may interact withand/or be executed within and/or be a component of compression process10 and/or storage management application 21. Examples of clientapplications 22, 24, 26, 28 may include, but are not limited to, e.g., astorage system application, a cloud computing application, a datasynchronization application, a data migration application, a garbagecollection application, or other application that allows for theimplementation and/or management of data in a clustered (ornon-clustered) environment (or the like), a standard and/or mobile webbrowser, an email application (e.g., an email client application), atextual and/or a graphical user interface, a customized web browser, aplugin, an Application Programming Interface (API), or a customapplication. The instruction sets and subroutines of client applications22, 24, 26, 28, which may be stored on storage devices 30, 32, 34, 36,coupled to client electronic devices 38, 40, 42, 44, may be executed byone or more processors and one or more memory architectures incorporatedinto client electronic devices 38, 40, 42, 44.

In some implementations, one or more of storage devices 30, 32, 34, 36,may include but are not limited to: hard disk drives; flash drives, tapedrives; optical drives; RAID arrays; random access memories (RAM); andread-only memories (ROM). Examples of client electronic devices 38, 40,42, 44 (and/or computer 12) may include, but are not limited to, apersonal computer (e.g., client electronic device 38), a laptop computer(e.g., client electronic device 40), a smart/data-enabled, cellularphone (e.g., client electronic device 42), a notebook computer (e.g.,client electronic device 44), a tablet, a server, a television, a smarttelevision, a smart speaker, an Internet of Things (IoT) device, a media(e.g., video, photo, etc.) capturing device, and a dedicated networkdevice. Client electronic devices 38, 40, 42, 44 may each execute anoperating system, examples of which may include but are not limited to,Android™, Apple® iOS®, Mac® OS X®; Red Hat® Linux®, Windows® Mobile,Chrome OS, Blackberry OS, Fire OS, or a custom operating system.

In some implementations, one or more of client applications 22, 24, 26,28 may be configured to effectuate some or all of the functionality ofcompression process 10 (and vice versa). Accordingly, in someimplementations, compression process 10 may be a purely server-sideapplication, a purely client-side application, or a hybridserver-side/client-side application that is cooperatively executed byone or more of client applications 22, 24, 26, 28 and/or compressionprocess 10.

In some implementations, one or more of client applications 22, 24, 26,28 may be configured to effectuate some or all of the functionality ofstorage management application 21 (and vice versa). Accordingly, in someimplementations, storage management application 21 may be a purelyserver-side application, a purely client-side application, or a hybridserver-side/client-side application that is cooperatively executed byone or more of client applications 22, 24, 26, 28 and/or storagemanagement application 21. As one or more of client applications 22, 24,26, 28, compression process 10, and storage management application 21,taken singly or in any combination, may effectuate some or all of thesame functionality, any description of effectuating such functionalityvia one or more of client applications 22, 24, 26, 28, compressionprocess 10, storage management application 21, or combination thereof,and any described interaction(s) between one or more of clientapplications 22, 24, 26, 28, compression process 10, storage managementapplication 21, or combination thereof to effectuate such functionality,should be taken as an example only and not to limit the scope of thedisclosure.

In some implementations, one or more of users 46, 48, 50, 52 may accesscomputer 12 and compression process 10 (e.g., using one or more ofclient electronic devices 38, 40, 42, 44) directly through network 14 orthrough secondary network 18. Further, computer 12 may be connected tonetwork 14 through secondary network 18, as illustrated with phantomlink line 54. Compression process 10 may include one or more userinterfaces, such as browsers and textual or graphical user interfaces,through which users 46, 48, 50, 52 may access compression process 10.

In some implementations, the various client electronic devices may bedirectly or indirectly coupled to network 14 (or network 18). Forexample, client electronic device 38 is shown directly coupled tonetwork 14 via a hardwired network connection. Further, clientelectronic device 44 is shown directly coupled to network 18 via ahardwired network connection. Client electronic device 40 is shownwirelessly coupled to network 14 via wireless communication channel 56established between client electronic device 40 and wireless accesspoint (i.e., WAP) 58, which is shown directly coupled to network 14. WAP58 may be, for example, an IEEE 802.11a, 802.11b, 802.11g, 802.11n,802.11ac, Wi-Fi®, RFID, and/or Bluetooth™ (including Bluetooth™ LowEnergy) device that is capable of establishing wireless communicationchannel 56 between client electronic device 40 and WAP 58. Clientelectronic device 42 is shown wirelessly coupled to network 14 viawireless communication channel 60 established between client electronicdevice 42 and cellular network/bridge 62, which is shown by exampledirectly coupled to network 14.

In some implementations, some or all of the IEEE 802.11x specificationsmay use Ethernet protocol and carrier sense multiple access withcollision avoidance (i.e., CSMA/CA) for path sharing. The various802.11x specifications may use phase-shift keying (i.e., PSK) modulationor complementary code keying (i.e., CCK) modulation, for example.Bluetooth™ (including Bluetooth™ Low Energy) is a telecommunicationsindustry specification that allows, e.g., mobile phones, computers,smart phones, and other electronic devices to be interconnected using ashort-range wireless connection. Other forms of interconnection (e.g.,Near Field Communication (NFC)) may also be used.

In some implementations, various I/O requests (e.g., I/O request 15) maybe sent from, e.g., client applications 22, 24, 26, 28 to, e.g.,computer 12. Examples of I/O request 15 may include but are not limitedto, data write requests (e.g., a request that content be written tocomputer 12) and data read requests (e.g., a request that content beread from computer 12).

Data Storage System:

Referring also to the example implementation of FIGS. 2-3 (e.g., wherecomputer 12 may be configured as a data storage system), computer 12 mayinclude storage processor 100 and a plurality of storage targets (e.g.,storage targets 102, 104, 106, 108, 110). In some implementations,storage targets 102, 104, 106, 108, 110 may include any of theabove-noted storage devices. In some implementations, storage targets102, 104, 106, 108, 110 may be configured to provide various levels ofperformance and/or high availability. For example, storage targets 102,104, 106, 108, 110 may be configured to form a non-fully-duplicativefault-tolerant data storage system (such as a non-fully-duplicative RAIDdata storage system), examples of which may include but are not limitedto: RAID 3 arrays, RAID 4 arrays, RAID 5 arrays, and/or RAID 6 arrays.It will be appreciated that various other types of RAID arrays may beused without departing from the scope of the present disclosure.

While in this particular example, computer 12 is shown to include fivestorage targets (e.g., storage targets 102, 104, 106, 108, 110), this isfor example purposes only and is not intended limit the presentdisclosure. For instance, the actual number of storage targets may beincreased or decreased depending upon, e.g., the level ofredundancy/performance/capacity required.

Further, the storage targets (e.g., storage targets 102, 104, 106, 108,110) included with computer 12 may be configured to form a plurality ofdiscrete storage arrays. For instance, and assuming for example purposesonly that computer 12 includes, e.g., ten discrete storage targets, afirst five targets (of the ten storage targets) may be configured toform a first RAID array and a second five targets (of the ten storagetargets) may be configured to form a second RAID array.

In some implementations, one or more of storage targets 102, 104, 106,108, 110 may be configured to store coded data (e.g., via storagemanagement process 21), wherein such coded data may allow for theregeneration of data lost/corrupted on one or more of storage targets102, 104, 106, 108, 110. Examples of such coded data may include but isnot limited to parity data and Reed-Solomon data. Such coded data may bedistributed across all of storage targets 102, 104, 106, 108, 110 or maybe stored within a specific storage target.

Examples of storage targets 102, 104, 106, 108, 110 may include one ormore data arrays, wherein a combination of storage targets 102, 104,106, 108, 110 (and any processing/control systems associated withstorage management application 21) may form data array 112.

The manner in which computer 12 is implemented may vary depending upone.g., the level of redundancy/performance/capacity required. Forexample, computer 12 may be configured as a SAN (i.e., a Storage AreaNetwork), in which storage processor 100 may be, e.g., a dedicatedcomputing system and each of storage targets 102, 104, 106, 108, 110 maybe a RAID device. An example of storage processor 100 may include but isnot limited to a VPLEX™, VNX™, or Unity™ system offered by Dell EMC™ ofHopkinton, Mass.

In the example where computer 12 is configured as a SAN, the variouscomponents of computer 12 (e.g., storage processor 100, and storagetargets 102, 104, 106, 108, 110) may be coupled using networkinfrastructure 114, examples of which may include but are not limited toan Ethernet (e.g., Layer 2 or Layer 3) network, a fiber channel network,an InfiniBand network, or any other circuit switched/packet switchednetwork.

As discussed above, various I/O requests (e.g., I/O request 15) may begenerated. For example, these I/O requests may be sent from, e.g.,client applications 22, 24, 26, 28 to, e.g., computer 12.Additionally/alternatively (e.g., when storage processor 100 isconfigured as an application server or otherwise), these I/O requestsmay be internally generated within storage processor 100 (e.g., viastorage management process 21). Examples of I/O request 15 may includebut are not limited to data write request 116 (e.g., a request thatcontent 118 be written to computer 12) and data read request 120 (e.g.,a request that content 118 be read from computer 12).

In some implementations, during operation of storage processor 100,content 118 to be written to computer 12 may be received and/orprocessed by storage processor 100 (e.g., via storage management process21). Additionally/alternatively (e.g., when storage processor 100 isconfigured as an application server or otherwise), content 118 to bewritten to computer 12 may be internally generated by storage processor100 (e.g., via storage management process 21).

As discussed above, the instruction sets and subroutines of storagemanagement application 21, which may be stored on storage device 16included within computer 12, may be executed by one or more processorsand one or more memory architectures included with computer 12.Accordingly, in addition to being executed on storage processor 100,some or all of the instruction sets and subroutines of storagemanagement application 21 (and/or compression process 10) may beexecuted by one or more processors and one or more memory architecturesincluded with data array 112.

In some implementations, storage processor 100 may include front endcache memory system 122. Examples of front end cache memory system 122may include but are not limited to a volatile, solid-state, cache memorysystem (e.g., a dynamic RAM cache memory system), a non-volatile,solid-state, cache memory system (e.g., a flash-based, cache memorysystem), and/or any of the above-noted storage devices.

In some implementations, storage processor 100 may initially storecontent 118 within front end cache memory system 122. Depending upon themanner in which front end cache memory system 122 is configured, storageprocessor 100 (e.g., via storage management process 21) may immediatelywrite content 118 to data array 112 (e.g., if front end cache memorysystem 122 is configured as a write-through cache) or may subsequentlywrite content 118 to data array 112 (e.g., if front end cache memorysystem 122 is configured as a write-back cache).

In some implementations, one or more of storage targets 102, 104, 106,108, 110 may include a backend cache memory system. Examples of thebackend cache memory system may include but are not limited to avolatile, solid-state, cache memory system (e.g., a dynamic RAM cachememory system), a non-volatile, solid-state, cache memory system (e.g.,a flash-based, cache memory system), and/or any of the above-notedstorage devices.

Storage Targets:

As discussed above, one or more of storage targets 102, 104, 106, 108,110 may be a RAID device. For instance, and referring also to FIG. 3,there is shown example target 150, wherein target 150 may be one exampleimplementation of a RAID implementation of, e.g., storage target 102,storage target 104, storage target 106, storage target 108, and/orstorage target 110. An example of target 150 may include but is notlimited to a VPLEX™, VNX™, or Unity™ system offered by Dell EMC™ ofHopkinton, Mass. Examples of storage devices 154, 156, 158, 160, 162 mayinclude one or more electro-mechanical hard disk drives, one or moresolid-state/flash devices, and/or any of the above-noted storagedevices. It will be appreciated that while the term “disk” or “drive”may be used throughout, these may refer to and be used interchangeablywith any types of appropriate storage devices as the context andfunctionality of the storage device permits.

In some implementations, target 150 may include storage processor 152and a plurality of storage devices (e.g., storage devices 154, 156, 158,160, 162). Storage devices 154, 156, 158, 160, 162 may be configured toprovide various levels of performance and/or high availability (e.g.,via storage management process 21). For example, one or more of storagedevices 154, 156, 158, 160, 162 (or any of the above-noted storagedevices) may be configured as a RAID 0 array, in which data is stripedacross storage devices. By striping data across a plurality of storagedevices, improved performance may be realized. However, RAID 0 arraysmay not provide a level of high availability. Accordingly, one or moreof storage devices 154, 156, 158, 160, 162 (or any of the above-notedstorage devices) may be configured as a RAID 1 array, in which data ismirrored between storage devices. By mirroring data between storagedevices, a level of high availability may be achieved as multiple copiesof the data may be stored within storage devices 154, 156, 158, 160,162.

While storage devices 154, 156, 158, 160, 162 are discussed above asbeing configured in a RAID 0 or RAID 1 array, this is for examplepurposes only and not intended to limit the present disclosure, as otherconfigurations are possible. For example, storage devices 154, 156, 158,160, 162 may be configured as a RAID 3, RAID 4, RAID 5 or RAID 6 array.

While in this particular example, target 150 is shown to include fivestorage devices (e.g., storage devices 154, 156, 158, 160, 162), this isfor example purposes only and not intended to limit the presentdisclosure. For instance, the actual number of storage devices may beincreased or decreased depending upon, e.g., the level ofredundancy/performance/capacity required.

In some implementations, one or more of storage devices 154, 156, 158,160, 162 may be configured to store (e.g., via storage managementprocess 21) coded data, wherein such coded data may allow for theregeneration of data lost/corrupted on one or more of storage devices154, 156, 158, 160, 162. Examples of such coded data may include but arenot limited to parity data and Reed-Solomon data. Such coded data may bedistributed across all of storage devices 154, 156, 158, 160, 162 or maybe stored within a specific storage device.

The manner in which target 150 is implemented may vary depending upone.g., the level of redundancy/performance/capacity required. Forexample, target 150 may be a RAID device in which storage processor 152is a RAID controller card and storage devices 154, 156, 158, 160, 162are individual “hot-swappable” hard disk drives. Another example oftarget 150 may be a RAID system, examples of which may include but arenot limited to an NAS (i.e., Network Attached Storage) device or a SAN(i.e., Storage Area Network).

In some implementations, storage target 150 may execute all or a portionof storage management application 21. The instruction sets andsubroutines of storage management application 21, which may be stored ona storage device (e.g., storage device 164) coupled to storage processor152, may be executed by one or more processors and one or more memoryarchitectures included with storage processor 152. Storage device 164may include but is not limited to any of the above-noted storagedevices.

As discussed above, computer 12 may be configured as a SAN, whereinstorage processor 100 may be a dedicated computing system and each ofstorage targets 102, 104, 106, 108, 110 may be a RAID device.Accordingly, when storage processor 100 processes data requests 116,120, storage processor 100 (e.g., via storage management process 21) mayprovide the appropriate requests/content (e.g., write request 166,content 168 and read request 170) to, e.g., storage target 150 (which isrepresentative of storage targets 102, 104, 106, 108 and/or 110).

In some implementations, during operation of storage processor 152,content 168 to be written to target 150 may be processed by storageprocessor 152 (e.g., via storage management process 21). Storageprocessor 152 may include cache memory system 172. Examples of cachememory system 172 may include but are not limited to a volatile,solid-state, cache memory system (e.g., a dynamic RAM cache memorysystem) and/or a non-volatile, solid-state, cache memory system (e.g., aflash-based, cache memory system). During operation of storage processor152, content 168 to be written to target 150 may be received by storageprocessor 152 (e.g., via storage management process 21) and initiallystored (e.g., via storage management process 21) within front end cachememory system 172.

Some storage systems may operate on, e.g., 8K block granularity. Thismay generally mean that compression is also done on 8K blocks.Typically, such compression is performed on individual blocks. However,applying compression to individual blocks may be less space efficientthan compressing multiple blocks together. Analysis was conducted withvarious data sets and varying the block count to check the compressionsavings for different aggregates. According to example and non-limitingresults, for blocks 12×8 k and larger, the savings change was less than0.5%. As well, the compression savings achieved with 4 k may only be 21%and 40% for 8 k. However, by compressing all 12 as a single segment,there may be a 58% savings. Therefore, as will be discussed below, thepresent disclosure may improve space efficiency using multi-blockaggregation, and may determine which blocks to select for themulti-block aggregation using one or more heuristics.

The Compression Process:

As discussed above and referring also at least to the exampleimplementations of FIGS. 4-5, compression process 10 may identify 400 aplurality of blocks. Compression process 10 may determine 402 at leastone heuristic associated with at least a portion of the plurality ofblocks. Compression process 10 may determine 404 whether to compress atleast the portion of the plurality of blocks based upon, at least inpart, the at least one heuristic. Compression process 10 may compress406 at least the portion of the plurality of blocks based upon, at leastin part, the at least one heuristic.

In some implementations, compression process 10 may identify 400 aplurality of blocks. For instance, and referring at least to the exampleimplementation of FIG. 5, an example basic mapping structure 500 diagramshows a set of blocks, as will be discussed below, may be identified andselected by compression process 10 to be aggregated and compressed. Itwill be appreciated that while a particular mapping structure is shownfor a particular storage system example, other storage system designsand implementations may be used without departing from the scope of thepresent disclosure. As such, the use of basic mapping structure 500 orany particular storage system should be taken as example only and not tootherwise limit the scope of the disclosure.

In the example, for compressed data, the data FSBN in the VBM may pointsto a compressed region, which often may be, e.g., 64K in size, but mayvary depending on block allocation, and if compressed regions have beensubject to evacuation (e.g., FS-R, SpaceMaker, etc.). Currently, forexample purposes only, each extent represents individual compressed AU,preceding by ZipHeader.

In some implementations, compression process 10 may determine 402 atleast one heuristic associated with at least a portion of the pluralityof blocks, and in some implementations, compression process 10 maydetermine 404 whether to compress at least the portion of the pluralityof blocks based upon, at least in part, the at least one heuristic. Forexample, the at least one heuristic may include a distance metricassociated with at least the portion of the plurality of blocks, and/ormay include a digital entropy value associated with at least the portionof the plurality of blocks. That is, compression process 10 may choosewhich blocks to group together for the purpose of compression based onvarious heuristics. For example, blocks that are close to each otherbased on some distance metrics or blocks that have similar digitalentropy.

As an example, the digital entropy value may determine whether tocompress at least the portion of the plurality of blocks, and thencompression process 10 may compress 406 at least the portion of theplurality of blocks based upon, at least in part, the at least oneheuristic. For instance, compression process 10 may use the digitalentropy value to decide which block (i.e., multiple blocks) should becompressed together for better space efficiency. These multi-blockcompressed segments may either be written contiguously or distributedacross different fragments (holes) to reduce fragmentation. It may bepossible that these larger segments may need to be available during readand decompression, which may hurt truly random read performance;however, no real-world workload is truly random and the fact thoseblocks were grouped during write may indicate they are related and maybe needed during read. As such, the present disclosure may also serveadvantageously as a sophisticated prefetch mechanism.

As noted above, the digital entropy value may be used to determine whichblocks to compress together. For example, entropy as applied to databuffers may measure randomness of data (or randomness distribution ofsymbols). The example and non-limiting equation to represent binary dataused to determine the digital entropy value for a block may be asfollows:

${H = {- {\sum\limits_{i = 0}^{255}{P_{i}{\log_{2}\left( P_{i} \right)}}}}},$

-   -   where Pn=(number of n-valued bytes)/(total number of bytes),        which may represent the probability of appearing n-valued bytes        in the dataset.

In the example, the equation may determine a resulting value ofsomething between, e.g., zero (0) and eight (8). In the example, thecloser a block's number is to zero for a block, the more orderly ornon-random the data is (and therefore there may be less of a need orbenefit to compress that particular block). Conversely, the closer thedata is to the value of eight for a block, the more random ornon-uniform the data is (and therefore there may be more of a need orbenefit to compress that particular block).

In some implementations, at least the portion of the plurality of blockswith a similar digital entropy value may be compressed together. Forinstance, assume for example purposes only that a first group of blockshad a resulting digital entropy value of 6, and a second group of blockshad a resulting digital entropy value of 8. In the example, because thefirst group of blocks had a similar entropy value (e.g., 6), the firstgroup of blocks may be compressed together. Similarly, because thesecond group of blocks had a similar entropy value (e.g., 8), the secondgroup of blocks may be compressed together.

In some implementations, the similar digital entropy value may beindicated by a range. For instance, assume for example purposes onlythat a first group of blocks had a resulting digital entropy value ofbetween 5-6, and a second group of blocks had a resulting digitalentropy value of 7-8. In the example, because each of the blocks in thefirst group of blocks had a similar entropy value (e.g., between 5-6),the first group of blocks may be compressed together. Similarly, becauseeach of the blocks in the second group of blocks had a similar entropyvalue (e.g., between 7-8), the second group of blocks may be compressedtogether.

In some implementations, a first level of compression may be used tocompress at least the portion of the plurality of blocks duringbackground operations, and a second level of compression may be used tocompress at least the portion of the plurality of blocks duringnon-background operations. For example, compression process 10 mayidentify whether a first phase (e.g., background operation) associatedwith a data operation is occurring with, e.g., a file or other object,or a second phase (e.g., a non-background or an inline phase) associatedwith the data operation is occurring with the file. In someimplementations, to make this determination, compression process 10 mayaccess file metadata of the file stored as part of the file metadata ofthe file. For example, in order to decide when to allocate more or lessresources for background and non-background operations, compressionprocess 10 may use the file server information and file attributes tochange from optimal IOs to optimal data reduction based on thestatistics available to the server (e.g., NAS server) from theapplication hosts (or elsewhere) regarding quality of service of fileaccess that may be otherwise invisible to the block arrays.

In some implementations, compression process 10 may increase theresources available for data reduction (e.g., compression) operationswhen the first phase is occurring with the file. For example, ratherthan decreasing the above-noted resources for data reduction operations,during the background operation, reduction process 10 may increase(e.g., allocate more) computation resources for data reduction when theCPU resources are freed from the IO load (i.e., no longer needed for theinline phase) which may then be used for higher data reduction, forinstance, if there is some indication (e.g., digital entropy metric ordistance metric) that the compression or re-compression will reduce thedata written to disk above a certain level.

In some implementations, compression process 10 may decrease resourcesavailable for data reduction operations when the second phase isoccurring with the file. That is, compression process 10 may balancebetween compression, IOPS, and latency, by using lighter inlinecompression. During the inline phases, compression process 10 may useless powerful data reduction methods to allow the highest IO performancewhen utilizing all the available CPU resources, including, e.g., lowerlevels of compression, 1z1, and fixed block size deduplication.

The terminology used herein is for the purpose of describing particularimplementations only and is not intended to be limiting of thedisclosure. As used herein, the singular forms “a”, “an” and “the” areintended to include the plural forms as well, unless the context clearlyindicates otherwise. As used herein, the language “at least one of A, B,and C” (and the like) should be interpreted as covering only A, only B,only C, or any combination of the three, unless the context clearlyindicates otherwise. It will be further understood that the terms“comprises” and/or “comprising,” when used in this specification,specify the presence of stated features, integers, steps (notnecessarily in a particular order), operations, elements, and/orcomponents, but do not preclude the presence or addition of one or moreother features, integers, steps (not necessarily in a particular order),operations, elements, components, and/or groups thereof.

The corresponding structures, materials, acts, and equivalents (e.g., ofall means or step plus function elements) that may be in the claimsbelow are intended to include any structure, material, or act forperforming the function in combination with other claimed elements asspecifically claimed. The description of the present disclosure has beenpresented for purposes of illustration and description, but is notintended to be exhaustive or limited to the disclosure in the formdisclosed. Many modifications, variations, substitutions, and anycombinations thereof will be apparent to those of ordinary skill in theart without departing from the scope and spirit of the disclosure. Theimplementation(s) were chosen and described in order to explain theprinciples of the disclosure and the practical application, and toenable others of ordinary skill in the art to understand the disclosurefor various implementation(s) with various modifications and/or anycombinations of implementation(s) as are suited to the particular usecontemplated.

Having thus described the disclosure of the present application indetail and by reference to implementation(s) thereof, it will beapparent that modifications, variations, and any combinations ofimplementation(s) (including any modifications, variations,substitutions, and combinations thereof) are possible without departingfrom the scope of the disclosure defined in the appended claims.

What is claimed is:
 1. A computer-implemented method comprising:identifying a plurality of blocks; determining at least one heuristicassociated with at least a portion of the plurality of blocks;determining whether to compress at least the portion of the plurality ofblocks based upon, at least in part, the at least one heuristic; andcompressing at least the portion of the plurality of blocks based upon,at least in part, the at least one heuristic.
 2. Thecomputer-implemented method of claim 1 wherein the at least oneheuristic includes a distance metric associated with at least theportion of the plurality of blocks.
 3. The computer-implemented methodof claim 1 wherein the at least one heuristic includes a digital entropyvalue associated with at least the portion of the plurality of blocks.4. The computer-implemented method of claim 3 wherein the digitalentropy value determines whether to compress at least the portion of theplurality of blocks.
 5. The computer-implemented method of claim 3wherein at least the portion of the plurality of blocks with a similardigital entropy value are compressed together.
 6. Thecomputer-implemented method of claim 1 wherein a first level ofcompression is used to compress at least the portion of the plurality ofblocks during background operations.
 7. The computer-implemented methodof claim 6 wherein a second level of compression is used to compress atleast the portion of the plurality of blocks during non-backgroundoperations.
 8. A computer program product residing on a computerreadable storage medium having a plurality of instructions storedthereon which, when executed across one or more processors, causes atleast a portion of the one or more processors to perform operationscomprising: identifying a plurality of blocks; determining at least oneheuristic associated with at least a portion of the plurality of blocks;determining whether to compress at least the portion of the plurality ofblocks based upon, at least in part, the at least one heuristic; andcompressing at least the portion of the plurality of blocks based upon,at least in part, the at least one heuristic.
 9. The computer programproduct of claim 8 wherein the at least one heuristic includes adistance metric associated with at least the portion of the plurality ofblocks.
 10. The computer program product of claim 8 wherein the at leastone heuristic includes a digital entropy value associated with at leastthe portion of the plurality of blocks.
 11. The computer program productof claim 10 wherein the digital entropy value determines whether tocompress at least the portion of the plurality of blocks.
 12. Thecomputer program product of claim 10 wherein at least the portion of theplurality of blocks with a similar digital entropy value are compressedtogether.
 13. The computer program product of claim 8 wherein a firstlevel of compression is used to compress at least the portion of theplurality of blocks during background operations.
 14. The computerprogram product of claim 13 wherein a second level of compression isused to compress at least the portion of the plurality of blocks duringnon-background operations.
 15. A computing system including one or moreprocessors and one or more memories configured to perform operationscomprising: identifying a plurality of blocks; determining at least oneheuristic associated with at least a portion of the plurality of blocks;determining whether to compress at least the portion of the plurality ofblocks based upon, at least in part, the at least one heuristic; andcompressing at least the portion of the plurality of blocks based upon,at least in part, the at least one heuristic.
 16. The computing systemof claim 15 wherein the at least one heuristic includes a distancemetric associated with at least the portion of the plurality of blocks.17. The computing system of claim 15 wherein the at least one heuristicincludes a digital entropy value associated with at least the portion ofthe plurality of blocks.
 18. The computing system of claim 17 whereinthe digital entropy value determines whether to compress at least theportion of the plurality of blocks.
 19. The computing system of claim 17wherein at least the portion of the plurality of blocks with a similardigital entropy value are compressed together.
 20. The computing systemof claim 15 wherein a first level of compression is used to compress atleast the portion of the plurality of blocks during backgroundoperations, and wherein a second level of compression is used tocompress at least the portion of the plurality of blocks duringnon-background operations.